A Visualization-Based Method for Adapting Industrial Design Standards and Specifications

Abstract

This invention discloses a visual visualization-based method for adapting industrial design standards and specifications. Addressing the industry pain points of traditional manual adaptation (low efficiency, high subjectivity, prone to missed or incorrect detections) and existing intelligent technologies (limited adaptation dimensions and inability to verify non-quantitative visual indicators), this invention constructs a dynamically iterative three-tiered visual standard knowledge base (national, industry, and enterprise levels), transforming textual specification clauses into visual rule maps and quantified constraint parameters. This invention performs differentiated preprocessing on multi-source industrial design data, utilizes improved machine vision algorithms to extract design visual features with high precision, combines a hierarchical weighted matching algorithm to complete standard comparison and deviation risk classification, accurately marks violation points through a layered 3D visualization mode, and intelligently outputs adaptation optimization solutions based on a multi-scenario strategy library. This invention establishes an iterative closed-loop verification and traceability archiving mechanism to achieve visualized intelligent adaptation throughout the entire industrial design process.

Description

Technical Field

[0001] This invention belongs to the field of digital adaptation technology for industrial design, specifically a method for adapting industrial design standards and specifications based on visual visualization. Background Technology

[0002] Industrial design is a core component of industrial product research and development and production. It encompasses multiple dimensions, including product structure design, appearance design, process design, and dimensional specification design. Its design outcomes must strictly comply with national mandatory standards, industry general specifications, and enterprise-customized design standards. It is a key prerequisite for ensuring product quality, production safety, and market compliance.

[0003] Currently, the standards and specifications system in the field of industrial design is complex, encompassing thousands of articles including mandatory national safety standards, industry-wide design specifications, manufacturing standards, appearance quality standards, and enterprise-customized design guidelines. These standards are scattered across various document systems, and include both quantitative parameter standards and qualitative textual constraints. The overall content is obscure, abstract, and highly technical, making it difficult for non-professionals to interpret accurately. The current mainstream approach to adapting design standards relies entirely on designers manually consulting standard manuals, checking design parameters item by item, and judging design compliance based on their professional experience. This traditional adaptation model has many inherent technical flaws and severely hinders the digital development of industrial design. First, the adaptation efficiency is extremely low. Designers need to spend more than 30% of their R&D time during the product development phase to sort out and verify various standard clauses. The process of standard review, parameter comparison, and compliance judgment is cumbersome, which significantly lengthens the product development iteration cycle and reduces the product launch efficiency. Second, manual adaptation has a high error rate and is highly subjective. Different designers interpret the same standard clause differently, which can easily lead to omissions in standard checks, parameter mismatches, misjudgments of qualitative rules, and oversight of minor compliance issues, resulting in hidden compliance defects in the design results. Third, the deviation positioning accuracy is insufficient. Manual methods can only identify obvious dimensional and structural violations and cannot accurately capture micron-level dimensional deviations, subtle errors in surface curvature, color value deviations, and minor discrepancies in assembly gaps. These subtle compliance issues will surface during subsequent product trial production, mold development, and mass production, leading to mold scrapping, product rework, and batch defects, significantly increasing enterprise R&D and production costs. Fourth, the adaptation process lacks standardized records, manual adaptation lacks complete verification ledgers, and design iteration, compliance review, quality traceability, and responsibility definition lack effective data support, which is detrimental to the establishment of a standardized system and product quality control. Fifth, traditional adaptation cannot adapt to non-quantitative standards. There are no clear qualitative standards for appearance, surface smoothness, color matching standardization, structural layout rationality, and process compatibility. Manual compliance judgment cannot be unified and standardized, resulting in frequent homogenization and non-standardization of design results.

[0004] To address the numerous shortcomings of manual adaptation, the industry is gradually introducing intelligent design adaptation assistance technologies. However, existing solutions have significant technical limitations and shortcomings. Currently, mainstream intelligent adaptation systems only support numerical comparison and verification of single quantitative parameters, and can only perform simple threshold comparisons for quantifiable parameters such as design dimensions, aperture size, and spacing values, resulting in extremely limited adaptation dimensions. For non-quantifiable, visual qualitative standards such as core industrial design aspects like appearance, surface curvature, color specifications, structural layout, process adaptation, and safety protection design, existing technologies cannot perform analysis and verification, failing to achieve full-dimensional compliance adaptation. Furthermore, the output format of existing verification results is limited to plain text reports and data lists. The existing methods for displaying deviation parameters lack visual positioning and contextualized presentation, making it difficult for designers to quickly identify non-compliant points in the design model and intuitively understand the causes of deviations and directions for rectification, resulting in extremely poor practicality. Furthermore, existing technologies lack tiered adaptation and closed-loop verification mechanisms, cannot distinguish the adaptation priorities of mandatory and recommended standards, cannot classify deviation risk levels, and cannot complete secondary verification after design corrections. The completeness and rigor of the adaptation are severely insufficient, failing to meet the high-precision, multi-dimensional, standardized, and traceable compliance adaptation requirements of modern industrial design. Based on the aforementioned deficiencies of existing technologies, this invention specifically develops a visual visualization-based industrial design standard and specification adaptation method, filling a technological gap in the industry.

[0005] In response to the shortcomings of the existing technologies, there is an urgent need for a method that can achieve full-dimensional, visualized, and intelligent adaptation of industrial design standards and specifications, and solve the industry pain points of low efficiency, large errors, incomplete coverage, and low visualization of traditional manual adaptation. Summary of the Invention

[0006] In order to overcome the shortcomings of existing technologies, this invention provides a visual visualization-based industrial design standard specification adaptation method, which effectively solves the problems of high labor costs, single verification dimensions, insufficient refinement, unintuitive result display, lack of intelligent optimization capabilities, inability to close the adaptation loop, and poor traceability of existing industrial design standard adaptation technologies.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for adapting industrial design standards and specifications based on visual visualization, comprising the following steps: S1. Construct a visual industrial design standard knowledge base, sort out and summarize national, industry and enterprise three-level industrial design standards and specifications, decompose text-descriptive standard rules into six categories of visual constraint parameters: geometric dimensions, appearance and shape, color specifications, structural layout, process adaptation and safety threshold, establish a one-to-one mapping relationship between standard rules and visual features, generate a standardized visual rule map and store it in the knowledge base. S2. Obtain the industrial design results data to be adapted. The design results data includes two-dimensional design drawings, three-dimensional solid models, and product appearance renderings. Through machine vision acquisition equipment and model analysis tools, multi-view visual data acquisition and preprocessing are performed on the design results to remove image noise, repair model defects and textures, and unify the design data format. S3. Based on the visual feature extraction algorithm, perform full-dimensional feature analysis on the preprocessed design result data, extract core visual feature parameters such as geometric contour, size parameters, structural position, color pixels, surface curvature, and assembly gap of the design model, and construct a visual feature dataset of the design to be adapted. S4. Call the visual industrial design standard knowledge base, perform a dimension-by-dimensional intelligent matching and comparison between the visual feature dataset of the design to be adapted and the standard visual rule map, calculate the standard adaptation deviation value of each feature parameter, and locate the non-adaptation points that are out of tolerance, inconsistent, or missing. S5. Through the 3D visualization rendering interface, all standard adaptation deviation points are highlighted, layered, and displayed in a pop-up window with the deviation values. At the same time, based on the preset adaptation optimization strategy, targeted design parameter correction schemes, structural optimization schemes, and appearance compliance adjustment schemes are automatically generated. S6. Synchronize the visualization adaptation results, deviation reports, and optimization solutions to the design terminal. Designers complete design corrections based on the visualization content. After the corrections are completed, steps S3-S5 are executed again to achieve closed-loop adaptation verification until the design results fully comply with the corresponding standards and specifications.

[0008] In the knowledge base construction process of step S1, a full-process logic of "standard decomposition - rule quantification - visual mapping - graph modeling - iterative update" is adopted to realize the digital and visual transformation of various standards and specifications. The specific steps are as follows: S11. Hierarchical entry of various industrial design standards and specifications, distinguishing between three categories of rules: general mandatory standards, industry-specific standards, and enterprise-customized standards. The standard rules are decomposed in a structured manner, redundant text information is removed, and core constraints are retained. S12. Establish a standard rule visualization conversion model to transform non-quantitative textual rules into visual graphical constraints, parameter threshold ranges, color card templates, and structural benchmark models, thereby realizing the visualization, quantification, and graphical representation of all standard rules. S13. Classify and encode the visualization standard rules, manage their versions, establish a dynamic update mechanism, synchronize the latest industry standards and enterprise design specifications in real time, and complete the iterative optimization of the knowledge base.

[0009] Preferably, in step S2, the data preprocessing is adapted to meet the unified parsing requirements of various types of industrial design output data, and differentiated preprocessing is performed for different data characteristics of 2D drawings, 3D models, and rendered images. Specifically, 2D image data preprocessing includes image grayscale conversion, binarization, adaptive denoising filtering, edge contour enhancement, and perspective distortion correction to eliminate noise, distortion, and blurring issues generated during modeling, screenshotting, and scanning. 3D model data preprocessing includes model topology repair, damaged surface completion, redundant structure removal, parameter normalization, and coordinate system unification to address issues such as inconsistent output model formats, inconsistent accuracy, and structural incompleteness from different modeling software, ensuring that all design data to be adapted can be uniformly parsed and features accurately extracted, guaranteeing the consistency and accuracy of subsequent adaptation verification.

[0010] Preferably, in step S3, the visual feature extraction algorithm employs an improved Mask R-CNN instance segmentation network combined with an optimized Canny contour detection algorithm. This approach lightweights and optimizes the accuracy of traditional algorithms, adding a subtle feature recognition module and an invalid feature filtering module. It can accurately identify the fine structure, edge contours, surface details, pixel colors, and other full-dimensional visual features of the design model, effectively avoiding the influence of redundant lines, blank pixels, auxiliary lines, and other interfering information on feature extraction. The feature extraction accuracy of this algorithm can reach 0.01mm, and the color pixel recognition accuracy can reach 16-bit color depth. It can accurately capture the fine-grained compliance deviations in industrial design, fully meeting the high-precision standardization adaptation requirements of high-end industrial products.

[0011] Preferably, in step S4, the intelligent matching and comparison employs a self-developed hierarchical weighted similarity matching algorithm, setting differentiated weight coefficients based on the mandatory level, risk level, and applicable scenarios of standards and specifications. Specifically, mandatory national security standards and production safety standards are assigned the highest weight to prioritize full-coverage verification and eliminate fatal compliance risks; industry-wide standards and process specifications are assigned medium weights to ensure the universality and standardization of product design; and enterprise-customized design standards and appearance optimization recommended standards are assigned regular weights to balance product standardization and design innovation. Simultaneously, based on the degree of deviation impact, the algorithm automatically distinguishes between three deviation levels: fatal deviation, general deviation, and optimization deviation, accurately determining the deviation risk level and providing a tiered basis for subsequent optimization and rectification.

[0012] Preferably, in step S5, the visualization display adopts a multi-level three-dimensional dynamic rendering mode, displaying data independently and interactively in four layers according to four dimensions: structural safety layer, dimensional compliance layer, appearance specification layer, and process adaptation layer. Data at each layer is independent and can be accessed and viewed separately. Simultaneously, a standardized deviation visualization identification system is established. Fatal deviations are highlighted in red with flashing lights, representing compliance risks and prohibiting production; general deviations are marked in yellow with standard markings, representing non-compliance issues requiring rectification; and optimization deviations are marked in light blue, representing areas that can be optimized and will not affect compliant production. All deviation points support click-and-interact functionality, with pop-up windows simultaneously displaying the specific deviation value, the range of exceedance, the original text of the corresponding standard clause, and an analysis of the causes of the violation. Adaptive optimization solutions are also displayed in conjunction, achieving integrated visualization adaptation of "problem location - basis query - solution rectification".

[0013] Preferably, in step S6, after the designer completes the design model correction based on the visual optimization scheme, the system automatically restarts the full-process adaptation verification to achieve iterative closed-loop adaptation until all deviations are cleared and the design results fully comply with the corresponding level of standards and specifications. After verification, the system automatically generates a standardized and traceable adaptation report. The report fully records the basic information of the design project, the list of adaptation standards, statistical data of deviations across all dimensions, comparison records before and after point-by-point corrections, compliance verification conclusions, and subsequent design optimization suggestions. It supports export in multiple formats, local archiving, cloud backup, and full-process traceability query, and can be directly used for project review, quality acceptance, design debriefing, and the establishment of enterprise standardization systems.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1) In practice, this invention breaks through the limitations of traditional technologies by realizing the full-dimensional visualization transformation of standard specifications. This invention innovatively constructs a three-level visualized standard knowledge base, and for the first time unifies purely textual qualitative standards, quantitative parameter standards, and implicit standards of appearance and process into visualized rule maps, benchmark models, and parameter threshold templates. This completely breaks the inherent mode of traditional design adaptation relying on manual textual interpretation and experience-based judgment, realizing the concrete, digital, and visualized presentation of abstract standards. This significantly lowers the professional threshold for industrial design standard adaptation, allowing even novice designers to quickly complete the standardization adaptation work. 2) In practice, this invention significantly improves the accuracy and completeness of verification by covering all dimensions of design adaptation scenarios. Relying on improved machine vision feature extraction technology, this invention can simultaneously complete compliance verification across six dimensions: geometric dimensions, appearance, color specifications, structural layout, process adaptation, and safety thresholds. It covers both traditional quantitative parameter verification scenarios and solves the problem of non-quantitative visual design compliance judgment that existing technologies cannot achieve. Simultaneously, its micron-level feature extraction accuracy can precisely capture subtle deviations that are undetectable by humans and traditional technologies, completely eliminating missed or incorrect detections and comprehensively ensuring the compliance and accuracy of industrial design results. 3) In practice, this invention significantly improves R&D efficiency through visual interactive adaptation and intelligent solution output. The invention employs a layered 3D visualization rendering and hierarchical deviation identification system, which can accurately locate compliance issues at any position on the model, intuitively displaying deviation data and the basis for violations, eliminating the need for manual verification of standards and problem investigation. Simultaneously, the system can automatically generate targeted and implementable parameter correction, structural fine-tuning, appearance optimization, and process adaptation rectification solutions based on different deviation types and product scenarios, greatly simplifying the design rectification process, shortening the product development iteration cycle, and effectively reducing production cost losses caused by design rework, mold scrapping, and batch defective products. 4) In practice, this invention utilizes a dynamic iteration + closed-loop traceability system, exhibiting strong adaptability and standardization. The visualized standard knowledge base of this invention supports real-time dynamic updates, quickly synchronizing with the latest national and industry standards and enterprise-customized specifications, adapting to the design standardization needs of different periods and industries. Simultaneously, the full-process closed-loop verification and end-to-end data traceability mechanism can completely retain data throughout the entire process of design adaptation, correction, and iteration, providing comprehensive data support for enterprise design standardization system construction, product quality review, project acceptance, and design review. Furthermore, this invention is adaptable to design scenarios for multiple categories of industrial products, unrestricted by product type, design dimension, or standard type, possessing strong industry versatility and engineering practical value. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0016] In the attached diagram: Figure 1 This is a flowchart illustrating a method for adapting industrial design standards and specifications based on visual visualization, as described in this invention. Figure 2 This is a schematic diagram of the S1 knowledge base visualization construction sub-process of the present invention; Figure 3 This is a schematic diagram of the S2-S6 intelligent adaptation and closed-loop iterative sub-processes of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] Example 1, by Figure 1 , Figure 2 and Figure 3 This paper presents a method for adapting industrial design standards and specifications based on visual visualization. The specific implementation steps are as follows: Step 1: Construct a visual, hierarchical, dynamic standard knowledge base Using the industrial design scenario of general mechanical equipment as the adaptable carrier, this paper comprehensively sorts out and summarizes national mandatory standards, general design specifications of the mechanical industry, and enterprise-specific customized design guidelines for mechanical product structural safety, dimensional tolerance, appearance quality, color specifications, assembly process, and safety protection. All standard clauses are structurally decomposed, categorized, and sorted, eliminating redundant explanatory text and retaining core constraints. They are then classified and organized according to six adaptation dimensions. For quantifiable standards such as dimensional tolerances, safety distances, and aperture thresholds, precise numerical threshold visualization range maps are established, marking upper limits, lower limits, and optimal adaptation values. For qualitative standards such as unquantifiable surface curvature, shape structure, and contour morphology, standardized three-dimensional benchmark visualization models are built as adaptation reference templates for design results. For product appearance color specifications, a dedicated color pixel threshold template is established by matching the national standard industrial color card system. For structural layout, assembly adaptation, and process production specifications, standardized structural benchmark maps and process constraint models are generated. Simultaneously, all visualization rules are hierarchically coded and version archived, distinguishing between mandatory standards, recommended standards, and enterprise-customized standards. A dynamically updated visualization standard knowledge base is built, which is automatically synchronized with the latest industry standard iterations regularly to ensure the timeliness and accuracy of the knowledge base rules. This study systematically examines national mandatory standards, industry-standard specifications, and enterprise-customized design standards related to mechanical product appearance design, structural safety, dimensional tolerances, and manufacturing processes. All textual clauses are broken down into six categories of visual constraint parameters. For quantitative standards related to dimensional tolerances, a visual range of numerical thresholds is established. For non-quantitative standards related to appearance shape and surface curvature, a visual standard benchmark model is generated. For color specification standards, a visual color constraint template is generated by matching national standard color cards. For structural layout and safety spacing standards, a visual structural benchmark map is generated. All visualization rules are hierarchically coded to distinguish between mandatory standards, recommended standards, and enterprise-customized standards. A dynamically updated visual standard knowledge base is built, synchronizing with the latest specification clauses in real time.

[0019] Step 2: Multi-source design data acquisition and standardized preprocessing Select the design results of the mechanical equipment housing to be adapted, and collect the product's 2D CAD engineering drawings, 3D SolidWorks model, and high-precision appearance rendering. Figure 3 Core design data is collected. For 2D drawings and renderings, multi-view image data is acquired using high-definition scanning equipment and precise screen capture tools to ensure image clarity and integrity. For 3D models, model data in a universal format is exported using modeling software, preserving complete structural, surface, and parameter information. Differential preprocessing is then performed: image data undergoes grayscale conversion, adaptive denoising, edge enhancement, and perspective correction to eliminate noise, blur, and perspective distortion generated during scanning and acquisition. 3D models undergo topology repair, damaged surface completion, redundant auxiliary structure removal, coordinate system unification, and parameter normalization to address data differences between different modeling formats and precision models, unifying the parsing standards of all data to be adapted and laying the data foundation for subsequent high-precision feature extraction. The process involves collecting design data such as 2D CAD drawings, 3D SolidWorks models, and product appearance renderings of the industrial products to be adapted. Supplementary image data acquisition is achieved using machine vision multi-view acquisition equipment. The acquired image data undergoes preprocessing including grayscale conversion, noise reduction filtering, and edge enhancement to eliminate noise interference from the shooting and modeling processes. The 3D models are then subjected to topological repair and parameter normalization to unify the parsing format of all design data, ensuring consistency and accuracy in subsequent feature extraction.

[0020] Step 3: Full-dimensional high-precision visual feature extraction and dataset construction An improved Mask R-CNN instance segmentation network combined with an optimized Canny contour detection algorithm is used to extract full-dimensional visual features from preprocessed multi-type design data. The focus is on accurately extracting over a hundred core visual feature parameters, including the overall geometric contour of the product shell, precise dimensions of each part, surface curvature parameters, corner radius, assembly hole spacing, internal structural layout, appearance color pixel values, surface flatness, and assembly gaps. During feature extraction, an invalid feature filtering module is activated to automatically remove interfering information such as model auxiliary lines, blank pixels, and redundant annotations, preventing invalid data from affecting adaptation accuracy. All extracted valid feature parameters are structured, categorized, and archived to construct a complete, accurate, and matchable visual feature dataset for the designs to be adapted. In this embodiment, the feature extraction accuracy is stably controlled at 0.01mm, and the color recognition error is less than one color depth unit, fully meeting the high-precision adaptation requirements of industrial products. An improved Mask R-CNN instance segmentation network combined with the Canny contour detection algorithm is used to extract full-dimensional visual features from preprocessed design data. Core feature parameters such as geometric contour dimensions, surface curvature, assembly gaps, structural positions, color pixel values, appearance arcs, and opening specifications of the product model are accurately extracted. Redundant lines and invalid pixels are filtered out, and a visual feature dataset containing over a hundred core parameters is constructed for the design to be adapted. The feature extraction accuracy reaches 0.01mm, meeting the high-precision adaptation requirements of industrial design.

[0021] Step 4: Graded Weighted Intelligent Matching and Deviation Grading Judgment The system calls upon the corresponding mechanical product design standard rule map from the visual standard knowledge base and employs a hierarchical weighted similarity matching algorithm to intelligently compare the visual feature dataset to be adapted with standard feature parameters and the visual benchmark model dimension by dimension and parameter by parameter. During the adaptation process, high-weight rules such as mandatory national safety standards and structural safety standards are prioritized for verification, followed by industry-wide specifications and enterprise-customized standards. The system accurately calculates the deviation difference and percentage between the actual value and the standard value of each feature parameter. Combining this with the degree of impact of the deviation on product safety, production process, and appearance quality, the system automatically classifies the deviation: deviations that affect product safety and prevent production are classified as fatal deviations; deviations that do not affect safety but do not comply with specifications and require rectification are classified as general deviations; and deviations that do not affect compliant production and only require optimization are classified as optimization deviations. Simultaneously, the system accurately locates the specific points and impact range of all deviations in the design model. The system calls upon the rule graph in the visual standard knowledge base and employs a weighted similarity matching algorithm to compare the design visual feature dataset with the standard feature parameters dimension by dimension. High-weight priority verification is set for mandatory safety standards and dimensional tolerance standards, while regular weight adaptation is set for appearance optimization recommendation standards. Based on the comparison results, the adaptation deviation value of each feature parameter is calculated, and the deviation is divided into three levels: fatal deviation, general deviation, and optimization deviation, to accurately locate all violation points and deviation ranges.

[0022] Step 5: Layered Visual Display and Intelligent Optimization Solution Generation A 3D visualization interactive interface is built, employing a layered dynamic rendering mode to independently display the adaptation and verification results across four levels. The structural safety layer focuses on displaying compliance deviations in the product shell's safety spacing, protective structures, and assembly structures; the dimensional compliance layer displays out-of-tolerance parameters and deviation ranges for dimensions, hole positions, and gaps in various parts; the appearance specification layer displays adaptation deviations in color values, surface curvature, contour shapes, and surface textures; and the process adaptation layer displays compliance issues in structural processes, assembly processes, and molding processes. Simultaneously, a standardized deviation identification system is implemented, with different levels of deviation highlighted in corresponding colors. Mouse hover and click interactions are supported, and pop-up windows accurately display the specific deviation values, out-of-tolerance ranges, the original text of the corresponding standard clauses, and an analysis of the causes of violations. Based on a built-in multi-scenario adaptation and optimization strategy library, the system automatically generates targeted parameter correction values, structural fine-tuning solutions, appearance optimization solutions, and process adaptation rectification solutions, taking into account the deviation type, product category, and standard requirements. These solutions are concrete and implementable, eliminating the need for designers to perform secondary simulations.

[0023] The system utilizes a 3D visualization interface and a layered rendering mode to display the adaptation results: the structural safety layer displays safety clearances and assembly structure deviations; the dimensional compliance layer displays dimensional deviation parameters; the appearance specification layer displays color, surface, and shape deviations; and the process adaptation layer displays process and structural compliance issues. Critical deviations are highlighted in red, general deviations in yellow, and optimization deviations in blue. Hovering the mouse over a window displays the deviation value, corresponding standard clause, and reason for violation. Simultaneously, based on built-in optimization strategies, the system automatically generates specific rectification plans for dimensional corrections, structural fine-tuning, appearance optimization, and process adaptation.

[0024] Step Six: Iterative Closed-Loop Verification and Standardized Traceability Report Generation Designers use the visual interface to identify deviations and intelligent optimization solutions to make targeted corrections and optimizations to 3D design models and 2D drawings. After correction, the system automatically restarts the entire process of visual feature extraction, intelligent matching and comparison, and visual verification to conduct secondary adaptation verification, forming an iterative closed-loop adaptation mechanism of "verification-deviation location-optimization correction-secondary verification". This iterative process continues until all deviations are cleared and the design results fully comply with all levels of standards and specifications. After the adaptation verification loop is completed, the system automatically generates a complete standardized adaptation traceability report, which records in detail the project name, designer, adaptation time, adaptation standard list, initial deviation statistics, comparison data before and after point-by-point correction, compliance verification conclusions, and subsequent design optimization suggestions. The report supports export to PDF and Word formats and cloud archiving, and can be directly used for project compliance review, product quality acceptance, design iteration review, and enterprise standardization system construction. After designers complete the design model correction based on the visualization optimization plan, they repeatedly execute the feature extraction, matching comparison, and visualization verification process until all deviations are cleared and the design results fully comply with the corresponding standards and specifications. After the adaptation is completed, the system automatically generates a standardized adaptation report, which records the basic design information, standard adaptation list, deviation statistics, correction process, and compliance conclusions. It supports PDF format export, cloud archiving, and full traceability, completing the closed-loop adaptation process.

Claims

1. A method for adapting industrial design standards and specifications based on visual visualization, characterized in that, Includes the following steps: S1. Construct a visual industrial design standard knowledge base, sort out and summarize national, industry and enterprise three-level industrial design standards and specifications, decompose text-descriptive standard rules into six categories of visual constraint parameters: geometric dimensions, appearance and shape, color specifications, structural layout, process adaptation and safety threshold, establish a one-to-one mapping relationship between standard rules and visual features, generate a standardized visual rule map and store it in the knowledge base. S2. Obtain the industrial design results data to be adapted. The design results data includes two-dimensional design drawings, three-dimensional solid models, and product appearance renderings. Through machine vision acquisition equipment and model analysis tools, multi-view visual data acquisition and preprocessing are performed on the design results to remove image noise, repair model defects and textures, and unify the design data format. S3. Based on the visual feature extraction algorithm, perform full-dimensional feature analysis on the preprocessed design result data, extract core visual feature parameters such as geometric contour, size parameters, structural position, color pixels, surface curvature, and assembly gap of the design model, and construct a visual feature dataset of the design to be adapted. S4. Call the visual industrial design standard knowledge base, perform a dimension-by-dimensional intelligent matching and comparison between the visual feature dataset of the design to be adapted and the standard visual rule map, calculate the standard adaptation deviation value of each feature parameter, and locate the non-adaptation points that are out of tolerance, inconsistent, or missing. S5. Through the 3D visualization rendering interface, all standard adaptation deviation points are highlighted, layered, and displayed in a pop-up window with the deviation values. At the same time, based on the preset adaptation optimization strategy, targeted design parameter correction schemes, structural optimization schemes, and appearance compliance adjustment schemes are automatically generated. S6. Synchronize the visualization adaptation results, deviation reports, and optimization solutions to the design terminal. Designers complete design corrections based on the visualization content. After the corrections are completed, steps S3-S5 are executed again to achieve closed-loop adaptation verification until the design results fully comply with the corresponding standards and specifications.

2. The method for adapting industrial design standards and specifications based on visual visualization according to claim 1, characterized in that, In step S1, the method for constructing the visualized industrial design standard knowledge base includes: S11. Hierarchical entry of various industrial design standards and specifications, distinguishing between three categories of rules: general mandatory standards, industry-specific standards, and enterprise-customized standards. The standard rules are decomposed in a structured manner, redundant text information is removed, and core constraints are retained. S12. Establish a standard rule visualization conversion model to transform non-quantitative textual rules into visual graphical constraints, parameter threshold ranges, color card templates, and structural benchmark models, thereby realizing the visualization, quantification, and graphical representation of all standard rules. S13. Classify and encode the visualization standard rules, manage their versions, establish a dynamic update mechanism, synchronize the latest industry standards and enterprise design specifications in real time, and complete the iterative optimization of the knowledge base.

3. The method for adapting industrial design standards and specifications based on visual visualization according to claim 1, characterized in that, In step S2, the data preprocessing adopts a differentiated processing strategy for multi-source heterogeneous data such as two-dimensional drawings, three-dimensional models, and appearance renderings. Specifically, it includes grayscale conversion, binarization, adaptive noise reduction filtering, edge contour enhancement, and perspective distortion correction of two-dimensional images, as well as topological structure repair, damaged surface patch completion, redundant auxiliary structure removal, parameter normalization, and three-dimensional coordinate system unification of three-dimensional models. This eliminates data differences caused by different modeling software and acquisition devices, and ensures that design data of different formats and accuracies can be uniformly adapted for parsing and feature extraction.

4. The method for adapting industrial design standards and specifications based on visual visualization according to claim 1, characterized in that, In step S3, the visual feature extraction algorithm adopts an improved Mask R-CNN instance segmentation network combined with an optimized Canny contour detection algorithm, and adds a subtle feature recognition module and an invalid feature filtering module. It can accurately identify the subtle structure, edge contours, surface details, pixel colors and other full-dimensional visual features of the design model, and effectively filter out interference information such as model auxiliary lines, blank pixels, and redundant annotations. The feature extraction accuracy reaches 0.01mm and the color pixel recognition accuracy reaches 16-bit color depth, ensuring the accuracy, effectiveness and refinement of the feature dataset.

5. The method for adapting industrial design standards and specifications based on visual visualization according to claim 1, characterized in that, In step S4, the intelligent matching comparison adopts a hierarchical weighted similarity matching algorithm. Differentiated weight coefficients are set according to the legal effect, security level, and application scenario of the standard specifications. National security mandatory standards have the highest weight and are given priority for full verification. Industry general standards and process specifications have the next highest weight, while enterprise customized standards and appearance optimization recommended standards have the lowest weight. At the same time, based on the degree of impact of deviations on product safety, production and compliant market launch, three types of deviation levels are automatically distinguished: fatal deviations, general deviations, and optimization deviations, to complete the risk classification judgment.

6. The method for adapting industrial design standards and specifications based on visual visualization according to claim 1, characterized in that, In step S5, the visualization display adopts a multi-level three-dimensional dynamic layered rendering mode, independently dividing four display dimensions: structural safety layer, size compliance layer, appearance specification layer, and process adaptation layer. Each layer can be independently accessed and interactively linked. A standardized color difference grading and labeling system is established, with fatal deviations marked with bright red flashing, general deviations marked with solid yellow bright, and optimization deviations marked with light blue. Clicking on the deviation point will simultaneously display the deviation value, the range of exceeding the standard, the original text of the corresponding standard clause, the cause of the violation, and preliminary correction suggestions.

7. The method for adapting industrial design standards and specifications based on visual visualization according to claim 1, characterized in that, In step S6, after the iterative closed-loop adaptation verification is completed, the system automatically generates a standardized and traceable adaptation report and adaptation ledger. The adaptation report includes basic project information, designer information, adaptation time, adaptation standard list, full-dimensional deviation statistics, comparison records before and after point-by-point correction, compliance verification conclusions, and iterative optimization suggestions. It supports export in multiple formats such as PDF and Word, local archiving, and cloud traceability query. The adaptation ledger can be directly used for product compliance review, project acceptance, quality review, and enterprise standardization system iteration.

8. The method for adapting industrial design standards and specifications based on visual visualization according to claim 2, characterized in that, In step S13, the dynamic update mechanism includes three types of update logic: standard incremental update, version iteration archiving, and invalid standard removal. It captures the latest released, revised, and abolished industrial design standard clauses from the national and industry levels in real time, updates the visual rule map and benchmark model synchronously, and marks and archives the differences between the standard versions before and after the iteration to ensure the timeliness, accuracy, and traceability of the knowledge base adaptation rules.

9. The method for adapting industrial design standards and specifications based on visual visualization according to claim 1, characterized in that, In step S5, the adaptation and optimization strategy has a built-in multi-scenario intelligent optimization strategy library. The optimization strategy library is associated with design parameter templates for multiple categories of industrial products such as machinery, home appliances, automobiles, and smart devices. For different types of deviations such as out-of-tolerance dimensions, irregular curved surfaces, out-of-tolerance colors, unreasonable structural layout, and incompatible processes, it automatically matches the optimal correction parameters for the corresponding category and generates a quantitative rectification plan that can be directly implemented without the need for manual secondary deduction and correction.

Images